UK Road Safety: Traffic Accidents and Vehicles Visualizations and Solution¶
The goal of this project is the investigate what causes Serious and Fatal accidents in hopes of preventing and decreasing the number of them. The dataset consists of accident records from the UK over the course of 15+ years. I hope to show the causes of these accidents through visualizations and create an algorithm that can predict the severity of accidents.
The UK government collects and publishes (usually on an annual basis) detailed information about traffic accidents across the country. This information includes, but is not limited to, geographical locations, weather conditions, type of vehicles, number of casualties and vehicle manoeuvres, making this a very interesting and comprehensive dataset for analysis and research.
The data that I'm using is compiled and available through Kaggle and in a less compliled form, here.
Genesis L. Taylor
Github | Linkedin | Tableau | genesisltaylor@gmail.com
Problem: Traffic Accidents
Solution Method: Use data to figure out how to lower the number of accidents and the severity of them.
Links to Other Notebooks¶
UK Road Safety: Traffic Accidents and Vehicles Introduction, Data Cleaning, and Feature Manipulation
UK Road Safety: Traffic Accidents and Vehicles Introduction, Data Cleaning, and Feature Manipulation: Github Link
UK Road Safety: Traffic Accidents and Vehicles Visualizations and Solution
UK Road Safety: Traffic Accidents and Vehicles Visualizations and Solution: Github Link
UK Road Safety: Traffic Accidents and Vehicles Machine Learning
UK Road Safety: Traffic Accidents and Vehicles Machine Learning: Github Link
Traffic Analysis and Severity Prediction Powerpoint Presentation
Traffic Analysis and Severity Prediction Powerpoint Presentation: Github Link
Importing and Data Merging¶
In [1]:
#Import modules
import numpy as np
import holidays
import pandas as pd
import seaborn as sns
import pickle
import time
import timeit
import matplotlib.pyplot as plt
plt.style.use('dark_background')
%matplotlib inline
import datetime
import math
from collections import Counter
#scipy
import scipy.stats as stats
from scipy import stats
from scipy.stats import chi2_contingency
#sklearn
import sklearn
from sklearn import ensemble
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler, OrdinalEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import resample
#for clustering
from sklearn.cluster import KMeans
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import silhouette_score
#other learners
from xgboost import XGBClassifier
import lightgbm as lgb
from kmodes.kmodes import KModes
#imblearn
from imblearn.ensemble import BalancedBaggingClassifier
from imblearn.ensemble import EasyEnsembleClassifier
from imblearn.ensemble import BalancedRandomForestClassifier
#webscraping
import requests
from bs4 import BeautifulSoup
import re
import urllib
from IPython.core.display import HTML
#time series
import statsmodels.api as sm
from pylab import rcParams
import itertools
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima_model import ARIMA
#warning ignorer
import warnings
warnings.filterwarnings("ignore")
In [2]:
# #DATAFRAME PICKLE CREATED IN CELLS BELOW INSTEAD OF RUNNING THROUGH ENTIRE PROCESS AFTER RESTARTING
# #import pickled file
# df = pd.read_pickle("df.pkl")
General Visualizations¶
In [278]:
accidentsperyear = df.groupby(['year'])['accident_index'].count()
# prepare plot
plt.style.use('dark_background')
plt.figure(figsize=(10,5))
colors = sns.color_palette("plasma", n_colors=7)
sns.barplot(accidentsperyear.index,accidentsperyear.values, palette=colors)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.title("Accidents Per Year",fontsize=20,fontweight="bold")
plt.xlabel("\nYear", fontsize=15, fontweight="bold")
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.savefig('accidentsperyear.png')
plt.tight_layout()
In [277]:
accidentspermonth = df.groupby(['month'])['accident_index'].count()
# prepare plot
plt.style.use('dark_background')
plt.figure(figsize=(20,10))
colors = sns.color_palette("plasma_r", n_colors=12)
mt=sns.barplot(accidentspermonth.index,accidentspermonth.values, palette=colors)
sns.despine(top=True, right=True, left=True, bottom=True)
#ax is the axes instance
group_labels = ['Jan', 'Feb','Mar','Apr','May','June','July','Aug','Sept','Oct','Nov','Dec' ]
mt.set_xticklabels(group_labels)
plt.title("Accidents Per Month",fontsize=20,fontweight="bold")
plt.xticks(fontsize=18)
plt.yticks(fontsize=12)
plt.xlabel("\nMonth", fontsize=15, fontweight="bold")
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.savefig('accidentspermonth.png')
plt.tight_layout()
In [276]:
weekdays = ['Monday', 'Tuesday','Wednesday','Thursday', 'Friday', 'Saturday', 'Sunday']
accweekday = df.groupby(['year', 'day_of_week']).size()
accweekday = accweekday.rename_axis(['year', 'day_of_week'])\
.unstack('day_of_week')\
.reindex(columns=weekdays)
plt.figure(figsize=(15,10))
plt.style.use('dark_background')
sns.heatmap(accweekday, cmap='plasma_r')
plt.title('\nAccidents by Weekday per Year\n', fontsize=14, fontweight='bold')
plt.xticks(fontsize=15)
plt.yticks(fontsize=12)
plt.xlabel('')
plt.ylabel('')
plt.savefig('accidentsbyweekdayperyear.png')
plt.show()
Fridays are the day of the week where the most accidents occur.
In [273]:
accidentsperseason = df.groupby(['season'])['accident_index'].count()
seaord=['spring', 'summer', 'fall','winter']
# prepare plot
plt.style.use('dark_background')
plt.figure(figsize=(15,10))
sns.barplot(accidentsperseason.index,accidentsperseason.values, order=seaord,
saturation=1, palette='magma_r')
sns.despine(top=True, right=True, left=True, bottom=True)
plt.title("Accidents Per Season",fontsize=20,fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=12)
plt.xlabel("\nSeason", fontsize=15, fontweight="bold")
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.tight_layout()
plt.savefig('accidentsperseason.png')
In [17]:
#"Morning Rush (6-10)", "Day (10-12)", "Lunch Rush (12-14)","Afternoon (14-16)",
#"After Work Rush (16-18)", "Evening (18-22)", "Night (22-6)"
timeofdaygroups = {'1': "Morning Rush",
'2': "Day",
'3': "Lunch Rush",
'4': "Afternoon",
'5': "After Work Rush",
'6': "Evening",
'7': "Night"}
df['time_of_day']=df['time_of_day'].map(timeofdaygroups)
In [267]:
accidentspertod = df.groupby(['time_of_day'])['accident_index'].count()
# prepare plot
plt.style.use('dark_background')
plt.figure(figsize=(15,10))
tod=["Morning Rush", "Day", "Lunch Rush", "Afternoon",
"After Work Rush", "Evening", "Night"]
sns.barplot(accidentspertod.index,accidentspertod.values, order=tod, palette='rainbow')
sns.despine(top=True, right=True, left=True, bottom=True)
plt.title("Accidents Per Time of Day",fontsize=20,fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=12)
plt.xlabel("", fontsize=15, fontweight="bold")
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.tight_layout()
plt.savefig('accidentspertod.png')
Accident Forecasting with Tableau¶
In [1]:
%%HTML
<div class='tableauPlaceholder' id='viz1572069627501' style='position: relative'><noscript><a href='#'><img alt=' ' src='https://public.tableau.com/static/images/Ac/AccidentForecasting/AccidentForecasting/1_rss.png' style='border: none' /></a></noscript><object class='tableauViz' style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='AccidentForecasting/AccidentForecasting' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='static_image' value='https://public.tableau.com/static/images/Ac/AccidentForecasting/AccidentForecasting/1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /></object></div> <script type='text/javascript'> var divElement = document.getElementById('viz1572069627501'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); </script>
Correlations¶
In [22]:
#correlation by accident severity pearson
corrdf=df.apply(LabelEncoder().fit_transform)
sc = StandardScaler()
corrdf = sc.fit_transform(corrdf)
corrdf=pd.DataFrame(data=corrdf,columns=df.columns)
corr=corrdf.corr()['accident_seriousness']
corr[np.argsort(corr,axis=0)[::-1]]
Out[22]:
In [23]:
corr_spear=corrdf.corr(method='spearman')['accident_seriousness']
corr_spear[np.argsort(corr_spear,axis=0)[::-1]]
Out[23]:
Looking at this I wanted to visualize some of the higher pos/negative correlations against accident severity.
Chi-Squared Test¶
Before these visualizations were done, I wanted to be sure that the visualizations were of some importance to accident_seriousness. For this, the chi-squared test was used.
In [41]:
"""chisquare algorithm from
http://www.insightsbot.com/blog/2AeuRL/chi-square-feature-selection-in-python """
class ChiSquare:
def __init__(self, dataframe):
self.df = dataframe
self.p = None #P-Value
self.chi2 = None #Chi Test Statistic
self.dof = None
self.dfObserved = None
self.dfExpected = None
def _print_chisquare_result(self, colX, alpha):
result = ""
if self.p<alpha:
result="The column {0} is IMPORTANT for Prediction".format(colX)
else:
result="The column {0} is NOT an important predictor. (Discard {0} from model)".format(colX)
print(result)
def TestIndependence(self,colX,colY, alpha=0.05):
X = self.df[colX].astype(str)
Y = self.df[colY].astype(str)
self.dfObserved = pd.crosstab(Y,X)
chi2, p, dof, expected = stats.chi2_contingency(self.dfObserved.values)
self.p = p
self.chi2 = chi2
self.dof = dof
self.dfExpected = pd.DataFrame(expected, columns=self.dfObserved.columns,
index = self.dfObserved.index)
self._print_chisquare_result(colX,alpha)
#Initialize ChiSquare Class
cT = ChiSquare(df)
#Feature Selection
testColumns = ['accident_index', '1st_road_class', '1st_road_number','2nd_road_number',
'carriageway_hazards', 'date', 'day_of_week',
'did_police_officer_attend_scene_of_accident','junction_control',
'junction_detail', 'latitude', 'light_conditions', 'local_authority_district',
'local_authority_highway', 'longitude','lsoa_of_accident_location',
'number_of_casualties', 'number_of_vehicles', 'pedestrian_crossing-human_control',
'pedestrian_crossing-physical_facilities', 'police_force','road_surface_conditions',
'road_type', 'special_conditions_at_site', 'speed_limit', 'time',
'urban_or_rural_area', 'weather_conditions', 'year', 'inscotland',
'age_band_of_driver', 'age_of_vehicle', 'driver_home_area_type',
'driver_imd_decile', 'engine_capacity_cc','hit_object_in_carriageway',
'hit_object_off_carriageway', 'journey_purpose_of_driver', 'junction_location',
'make', 'model','propulsion_code', 'sex_of_driver', 'skidding_and_overturning',
'towing_and_articulation', 'vehicle_leaving_carriageway',
'vehicle_locationrestricted_lane', 'vehicle_manoeuvre','vehicle_reference',
'vehicle_type', 'was_vehicle_left_hand_drive', 'x1st_point_of_impact', 'month',
'weekend', 'hour', 'time_of_day','season', 'engine_capacity_cc_size']
for var in testColumns:
cT.TestIndependence(colX=var,colY="accident_seriousness" )
Visualizations In Relation to Accident Seriousness¶
Method:¶
For my visualizations I have decided to use some of the features with the highest correlations to accident_seriousness:
- did_police_officer_attend_scene_of_accident
- x1st_point_of_impact
- number_of_vehicles
- speed_limit
- urban_or_rural_area
- skidding_and_overturning
- vehicle_leaving_carriageway
- sex_of_driver
- vehicle_type
- vehicle_manoeuvre
- engine_capacity_cc
- number_of_casualties
- driver_home_area_type
- age_band_of_driver
- junction_control
- hit_object_off_carriageway
- hit_object_in_carriageway
- driver_imd_decile *
- junction_detail *
- junction_location *
- propulsion_code *
- year *
Note: The columns used were selected because of the absolute value of their correlation in relation to accident_seriousness
*columns added after correlation was done after undersampling
For visual reasons, two separate dataframes were created, for not serious and serious accidents. I wanted to better scale the data and for me, this was the simplest way of doing so without resampling.
In [3]:
#dataframe where accidents are Slight
not_serious = df[(df['accident_seriousness']=="Not Serious")]
print("Not Serious Group Shape:", not_serious.shape)
not_serious.accident_seriousness.value_counts()
Out[3]:
In [4]:
#dataframe where accidents are serious
serious= df[(df['accident_seriousness']=="Serious")]
print("Serious Group Shape:", serious.shape)
serious.accident_seriousness.value_counts()
Out[4]:
In [20]:
#map 1, 2, 3 in did_police_officer_attend_scene_of_accident with Yes, No,Self-reported
policeattend = {1: "Yes", 2:"No", 3:"Self-Reported"}
not_serious['did_police_officer_attend_scene_of_accident']=not_serious['did_police_officer_attend_scene_of_accident'].map(policeattend)
df['did_police_officer_attend_scene_of_accident']=df['did_police_officer_attend_scene_of_accident'].map(policeattend)
serious['did_police_officer_attend_scene_of_accident']=serious['did_police_officer_attend_scene_of_accident'].map(policeattend)
In [21]:
imddecile = {1:"Most deprived 10%", 2:"More deprived 10-20%", 3:"More deprived 20-30%",
4:"More deprived 30-40%", 5:"More deprived 40-50%", 6:"Less deprived 40-50%",
7:"Less deprived 30-40%", 8:"Less deprived 20-30%", 9:"Less deprived 10-20%",
10:"Least deprived 10%"}
not_serious['driver_imd_decile']=not_serious['driver_imd_decile'].map(imddecile)
df['driver_imd_decile']=df['driver_imd_decile'].map(imddecile)
serious['driver_imd_decile']=serious['driver_imd_decile'].map(imddecile)
In [22]:
#setups for adding frequencies to visualizations
dftotal= float(len(df))
nstotal= float(len(not_serious))
setotal= float(len(serious))
Did Police Officer Attend Scene Of Accident¶
In [101]:
#Did Police Officer Attend Scene Of Accident
plt.figure(figsize=(15,10))
ax = sns.countplot("did_police_officer_attend_scene_of_accident", hue="accident_seriousness",
palette="PuBu", data=not_serious)
plt.title("Did Police Officer Attend Scene Of Not Serious Accident",
fontsize=20, fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nAttendance", fontsize=15, fontweight="bold")
plt.legend(fontsize=15, bbox_to_anchor=(1.0, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber Attended", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('did_police_officer_attend_scene_of_accident_not_serious.png')
plt.show()
#Did Police Officer Attend Scene Of Accident
plt.figure(figsize=(15,10))
ax = sns.countplot("did_police_officer_attend_scene_of_accident", hue="accident_seriousness",
palette="PuBu", data=serious)
plt.title("Did Police Officer Attend Scene Of Serious Accident",
fontsize=20, fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nAttendance", fontsize=15, fontweight="bold")
plt.legend(fontsize=15, bbox_to_anchor=(1.0, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber Attended", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('did_police_officer_attend_scene_of_accident_serious.png')
plt.show()
First Point of Impact Vs Accident Seriousness¶
In [102]:
# First Point of Impact Vs Accident Seriousness (Not Serious)
fpoa_order =["Front", "Nearside", "Did not impact", "Back", "Offside"]
plt.figure(figsize=(20,10))
ax = sns.countplot("x1st_point_of_impact", hue="accident_seriousness", order=fpoa_order,
palette="PuBu", data=not_serious)
plt.title("First Point of Impact in Not Serious Accidents",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nPoint of Impact", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nFirst Point of Impact Count", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('x1st_point_of_impact_not_serious.png')
plt.show()
# First Point of Impact Vs Accident Seriousness
plt.figure(figsize=(20,10))
ax = sns.countplot("x1st_point_of_impact", hue="accident_seriousness", order=fpoa_order,
palette="PuBu", data=serious)
plt.title("First Point of Impact in Serious Accidents",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nPoint of Impact", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nFirst Point of Impact Count", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('x1st_point_of_impact_serious.png')
plt.show()
Number of Vehicles¶
In [103]:
#number of vehicles vs accidentseriousness
nov_order=["1","2", "3", "4+"]
#notserious
plt.figure(figsize=(20,10))
ax = sns.countplot("accident_seriousness", hue="number_of_vehicles", hue_order=nov_order,
palette="GnBu_d", data=not_serious)
plt.style.use('dark_background')
plt.title("Number of Vehicles in Not Serious Accidents",
fontsize=20, fontweight="bold")
plt.xlabel("\nNumber of Vehicles", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('number_of_vehicles_not_serious.png')
plt.show()
#serious
plt.figure(figsize=(20,10))
ax = sns.countplot("accident_seriousness", hue="number_of_vehicles", hue_order=nov_order,
palette="GnBu_d", data=serious)
plt.style.use('dark_background')
plt.title("Number of Vehicles in Serious Accidents",
fontsize=20, fontweight="bold")
plt.xlabel("\nNumber of Vehicles", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('number_of_vehicles_serious.png')
plt.show()
Speed Limit vs Accident Seriousness¶
In [111]:
#notserious
splt_order=[15.0, 20.0,30.0,40.0 ,50.0,60.0, 70.0]
#splt1_order=[20.0,30.0,40.0 ,50.0,60.0, 70.0]
plt.figure(figsize=(20,10))
ax = sns.countplot("speed_limit", hue="accident_seriousness", order=splt_order,
palette="PuBu", data=not_serious)
plt.title("Speed Limit vs Not Serious Accidents",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nSpeed Limits", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nCount", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.4f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('speed_limit_not_serious.png')
plt.show()
#erious
plt.figure(figsize=(20,10))
ax = sns.countplot("speed_limit", hue="accident_seriousness",
palette="PuBu", data=serious)
plt.title("Speed Limit vs Serious Accidents",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nSpeed Limits", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nCount", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('speed_limit_serious.png')
plt.show()
Urban or Rural Area vs Accident Seriousness¶
In [112]:
#urban_or_rural_area vs accident seriousness
plt.figure(figsize=(20,10))
ax = sns.countplot("accident_seriousness", hue="urban_or_rural_area",
palette="PuBu", data=not_serious)
plt.title("Urban or Rural Area vs Accident Severity",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nSeverity", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nUrban or Rural Area Count", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('urban_or_rural_area_not_serious.png')
plt.show()
#urban_or_rural_area vs accident seriousness
plt.figure(figsize=(20,10))
ax = sns.countplot("accident_seriousness", hue="urban_or_rural_area",
palette="PuBu", data=serious)
plt.title("Urban or Rural Area vs Accident Severity",fontsize=20,fontweight="bold")
plt.style.use('dark_background')
plt.xlabel("\nSeverity", fontsize=15, fontweight="bold")
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nUrban or Rural Area Count", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('urban_or_rural_area_serious.png')
plt.show()
Skidding and Overturning vs Seriousness¶
In [116]:
#Not Serious Accident
sao_order=["None", "Skidded", "Skidded and overturned", "Overturned", "Jackknifed",
"Jackknifed and overturned"]
plt.figure(figsize=(15,10))
ax = sns.countplot("accident_seriousness", hue="skidding_and_overturning", hue_order=sao_order,
palette="magma", data=not_serious)
plt.style.use('dark_background')
plt.title("Skidding and Overturning in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Skidding and Overturning", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('skidding_and_overturning_not_serious.png')
plt.show()
#Serious Accident Manuevers
plt.figure(figsize=(15,10))
ax= sns.countplot("accident_seriousness", hue="skidding_and_overturning", hue_order=sao_order,
palette="magma", data=serious)
plt.style.use('dark_background')
plt.title("Skidding and Overturning in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Skidding and Overturning", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('skidding_and_overturning_serious.png')
plt.show()
Vehicle Leaving Carriageway vs Seriousness¶
In [118]:
#Not Serious Accident Manuevers
vlc_order=["Did not leave carriageway", "Straight ahead at junction", "Nearside",
"Offside", "Offside on to central reservation", "Nearside and rebounded",
"Offside - crossed central reservation", "Offside and rebounded",
"Offside on to centrl res + rebounded"]
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_leaving_carriageway", hue_order=vlc_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Vehicle Leaving Carriageway in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Vehicle Leaving Carriageway ", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents\n", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_leaving_carriageway_not_serious.png')
plt.show()
#Serious Accident Manuevers
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_leaving_carriageway", hue_order=vlc_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Vehicle Leaving Carriageway in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Vehicle Leaving Carriageway ", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents\n", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.3f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_leaving_carriageway_serious.png')
plt.show()
Sex of Driver vs Seriousness¶
In [121]:
#sex_of_driver
sod_order=["Female", "Male", "Not known"]
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="sex_of_driver", hue_order=sod_order,
palette="magma", data=not_serious)
plt.style.use('dark_background')
plt.title("Sex of Driver in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSex of Driver", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('sex_of_driver_not_serious.png')
plt.show()
#sex_of_driver serious
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="sex_of_driver", hue_order=sod_order,
palette="magma", data=serious)
plt.style.use('dark_background')
plt.title("Sex of Driver in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSex of Driver", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('sex_of_driver_serious.png')
plt.show()
In [122]:
#sex_of_driver
df['sex_of_driver'].value_counts()/df.shape[0]*100
Out[122]:
Vehicle Type vs Seriousness¶
In [126]:
#Not Serious Accident Type
vt_order=['Bus', 'Car', 'Goods Vehicle', 'Motorcycle', 'Other Vehicle']
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_type", hue_order=vt_order,
palette="tab20", data=not_serious)
plt.style.use('dark_background')
plt.title("Vehicle Type in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accidents by Vehicle Type", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_type_not_serious.png')
plt.show()
#Serious Accident Type
plt.figure(figsize=(15,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_type", hue_order=vt_order,
palette="tab20", data=serious)
plt.style.use('dark_background')
plt.title("Vehicle Type in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accidents by Vehicle Type", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_type_serious.png')
plt.show()
Vehicle Manoeuvres¶
In [128]:
#Not Serious Accident Manuevers
vm_order=['Turning right', 'Going ahead other', 'Going ahead right-hand bend',
'Slowing or stopping', 'Turning left', 'Waiting to go - held up',
'Waiting to turn right', 'Overtaking static vehicle - offside' ,
'Parked', 'Overtaking - nearside', 'U-turn', 'Changing lane to right',
'Reversing', 'Waiting to turn left', 'Changing lane to left',
'Going ahead left-hand bend', 'Overtaking moving vehicle - offside', 'Moving off']
plt.figure(figsize=(20,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_manoeuvre", hue_order=vm_order,
palette="tab20", data=not_serious)
plt.style.use('dark_background')
plt.title("Vehicle Manuevers in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Vehicle Manuevers", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_manoeuvre_not_serious.png')
plt.show()
#Serious Accident Manuevers
plt.figure(figsize=(20,10))
ax=sns.countplot("accident_seriousness", hue="vehicle_manoeuvre",hue_order=vm_order,
palette="tab20", data=serious)
plt.style.use('dark_background')
plt.title("Vehicle Manuevers in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Vehicle Manuevers", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('vehicle_manoeuvre_serious.png')
plt.show()
Driver Home Type Area¶
In [130]:
#driver_home_area_type
dhoa_order=['Urban area', 'Rural', 'Small town']
#Serious Accident Driver Home Type Area
plt.figure(figsize=(20,15))
ax= sns.countplot("accident_seriousness", hue="driver_home_area_type", hue_order=dhoa_order,
palette="rainbow", data=not_serious)
plt.style.use('dark_background')
plt.title("Accident Driver Home Type Area in Not Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSeriousness", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
#plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('driver_home_area_type_not_serious.png')
plt.show()
#driver_home_area_type
#Serious Accident Driver Home Type Area
plt.figure(figsize=(20,15))
ax= sns.countplot("accident_seriousness", hue="driver_home_area_type", hue_order=dhoa_order,
palette="rainbow", data=serious)
plt.style.use('dark_background')
plt.title("Accident Driver Home Type Area in Serious Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nSeriousness", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
#plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('driver_home_area_type_serious.png')
plt.show()
Age Band of Driver¶
In [131]:
#age_band_of_driver
abod_order=['Under 16', '16-25', '26-45', '46-65','Over 65']
#Not Serious Accident age_band_of_driver
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="age_band_of_driver", hue_order=abod_order,
palette="magma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Age Band of Driver",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Age Band of Driver", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
#plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('age_band_of_driver_not_serious.png')
plt.show()
#Serious Accident age_band_of_driver
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="age_band_of_driver", hue_order=abod_order,
palette="magma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Age Band of Driver",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Age Band of Driver", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
#plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('age_band_of_driver_serious.png')
plt.show()
Junction Control¶
In [133]:
#junction_control
jc_order = ['Give way or uncontrolled', 'Auto traffic signal', 'Authorised person',
'Stop sign','Not at junction or within 20 metres']
#Not Serious Accident junction_control
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_control", hue_order=jc_order,
palette="magma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Junction Control",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Junction Control", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_control_not_serious.png')
plt.show()
#Serious Accident junction_control
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_control",hue_order=jc_order,
palette="magma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Junction Control",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Junction Control", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_control_serious.png')
plt.show()
Hit Object Off Carriageway¶
In [135]:
#hit_object_off_carriageway
hooffc_order=['None', 'Lamp post', 'Road sign or traffic signal', 'Other permanent object',
'Entered ditch', 'Tree', 'Near/Offside crash barrier','Central crash barrier',
'Bus stop or bus shelter', 'Telegraph or electricity pole', 'Submerged in water',
'Wall or fence']
#Not Serious Accident hit_object_off_carriageway
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="hit_object_off_carriageway", hue_order=hooffc_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Hit Object Off Carriageway",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Hit Object Off Carriageway", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('hit_object_off_carriageway_not_serious.png')
plt.show()
#Serious Accident hit_object_off_carriageway
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="hit_object_off_carriageway", hue_order=hooffc_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Hit Object Off Carriageway",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Hit Object Off Carriageway", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('hit_object_off_carriageway_serious.png')
plt.show()
Hit Object In Carriageway¶
In [222]:
#hit_object_in_carriageway
hoinc_order=['None', 'Kerb', 'Other object', 'Bollard or refuge', 'Parked vehicle',
'Road works', 'Open door of vehicle', 'Central island of roundabout',
'Previous accident', 'Bridge (side)', 'Any animal (except ridden horse)',
'Bridge (roof)']
#Not Serious Accident hit_object_in_carriageway
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="hit_object_in_carriageway", hue_order=hoinc_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Hit Object in Carriageway",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Hit Object in Carriageway", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('hit_object_in_carriageway_not_serious.png')
plt.show()
#Serious Accident hit_object_in_carriageway
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="hit_object_in_carriageway", hue_order=hoinc_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Hit Object in Carriageway",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Hit Object in Carriageway", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('hit_object_in_carriageway_serious.png')
plt.show()
Driver IMD Decile¶
In [221]:
#driver_imd_decile
imd_order=["Least deprived 10%", "Less deprived 10-20%", "Less deprived 20-30%",
"Less deprived 30-40%","Less deprived 40-50%","Most deprived 10%",
"More deprived 10-20%", "More deprived 20-30%", "More deprived 30-40%",
"More deprived 40-50%"]
#Not Serious Accident driver_imd_decile
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="driver_imd_decile", hue_order=imd_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Driver Area Deprivation Score",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Driver Area Deprivation Score", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('driver_imd_decile_not_serious.png')
plt.show()
#Serious Accident driver_imd_decile
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="driver_imd_decile", hue_order=imd_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Driver Area Deprivation Score",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Driver Area Deprivation Score", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('driver_imd_decile_serious.png')
plt.show()
Junction Detail¶
In [210]:
#junction_detail
jud_order=['T or staggered junction', 'Mini-roundabout', 'Crossroads',
'Private drive or entrance', 'More than 4 arms (not roundabout)',
'Roundabout', 'Slip road', 'Other junction','Not at junction or within 20 metres']
#Not Serious Accident junction_detail
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_detail", hue_order=jud_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Junction Detail",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Junction Detail", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_detail_not_serious.png')
plt.show()
#Serious Accident junction_detail
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_detail", hue_order=jud_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Junction Detail",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Junction Detail", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_detail_serious.png')
plt.show()
Junction Location¶
In [211]:
#junction_location
jul_order=['Mid Junction - on roundabout or on main road', 'Entering main road',
'Approaching junction or waiting/parked at junction approach',
'Cleared junction or waiting/parked at junction exit', 'Leaving main road',
'Leaving roundabout', 'Entering roundabout', 'Entering from slip road',
'Not at or within 20 metres of junction']
#Not Serious Accident junction_location
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_location", hue_order=jul_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Junction Location",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Junction Location", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_location_not_serious.png')
plt.show()
#Serious Accident junction_location
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="junction_location", hue_order=jul_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Junction Location",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Junction Location", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('junction_location_serious.png')
plt.show()
Propulsion Code¶
In [218]:
#propulsion_code
pd_order=['Petrol', 'Heavy oil', 'Hybrid electric', 'Bio-fuel', 'LPG Petrol', 'Diesel',
'Fuel cells', 'New fuel technology', 'Electric diesel']
pd_order2=['Petrol', 'Heavy oil', 'Hybrid electric', 'Bio-fuel', 'LPG Petrol', 'Electric diesel']
#Not Serious Accident propulsion_code
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="propulsion_code", hue_order=pd_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Propulsion Code",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Propulsion Code", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('propulsion_code_not_serious.png')
plt.show()
#Serious Accident propulsion_code
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="propulsion_code", hue_order=pd_order2,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Propulsion Code",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Propulsion Code", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('propulsion_code_serious.png')
plt.show()
Year¶
In [230]:
#yeare
year_order=[2010, 2011, 2012, 2013, 2014, 2015, 2016]
#Not Serious Accident yeare
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="year", hue_order=year_order,
palette="plasma", data=not_serious)
plt.style.use('dark_background')
plt.title("Not Serious Accident by Year",fontsize=25,fontweight="bold")
plt.xlabel("\nNot Serious Accident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/nstotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('year_not_serious.png')
plt.show()
#Serious Accident year
plt.figure(figsize=(20,15))
ax=sns.countplot("accident_seriousness", hue="year", hue_order=year_order,
palette="plasma", data=serious)
plt.style.use('dark_background')
plt.title("Serious Accident by Year",fontsize=25,fontweight="bold")
plt.xlabel("\nSerious Accident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
for p in ax.patches:
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.2f}%'.format(height/setotal*100),
ha="center",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=True)
plt.savefig('year_serious.png')
plt.show()
Visualization Summary¶
- did_police_officer_attend_scene_of_accident: Police attended most accidents but were less likely to NOT be called in serious accidents.
- x1st_point_of_impact: Majority of accidents were front impacted as the first point of impact. Not serious accidents had a higher percentage of Back impact accidents than serious accidents. Serious accidents had higher percentages of Offside and Nearside accidents.
- number_of_vehicles: Nothing significant.
- speed_limit: Majority of accidents occurred in 30 speed limit zones. It would have been beneficial to have actual data on the speeds of the vehicles involved or at least if they were speeding.
- urban_or_rural_area: Rural areas had a higher percentage of serious accidents. This may relate to hospital locations or emergency vehicle arrival data which was not available.
- skidding_and_overturning: Higher percentages of serious accidents involved skidding, jackknifing or overturning.
- vehicle_leaving_carriageway: Most vehicles did not leave the carriageway in either type of accident, however serious accidents had higher percentages of those that did leave the carriageway.
- sex_of_driver: Men were more involved in both serious and not serious accidents, however according to racfoundation.org, there are only 355 of female privately registered cars on UK roads.
- vehicle_type: Motorcycles were involved in a significantly higher percentage of serious accidents than not serious accidents
- vehicle_manoeuvre: Nothing significant.
- driver_home_area_type: Rural and Small Towns has higher percentages of serious accidents. This may relate to hospital locations or emergency vehicle arrival data which was not available.
- age_band_of_driver: The age bands over the age of 25 had a higher percentage of serious accidents than not serious.
- junction_control: Most areas with accidents were uncontrolled.
- hit_object_off_carriageway: The majority of accidents did not involve objects being hit off the carriageway, however serious accidents had higher percentages of accidents that did involve hitting an object off the carriageway.
- hit_object_in_carriageway: Most accidents did not involve objects being hit in the carriageway; however serious accidents had higher percentages of accidents that did involve hitting an object off the carriageway.
- driver_imd_decile: Nothing significant. Most accidents occurred in areas that were Less deprived 20-30%
- junction_detail: T or staggered junctions were where most of the accidents occurred.
- junction_location: Nothing that separates the two serious types. However, most accidents seem to have occurred in Mid Junction - on roundabout or on main road or situations where the driver was approaching junction or waiting/parked at junction approach.
- propulsion_code: Diesel, Fuel cells, New fuel technology, vehicles were not recorded as a part of serious accidents.
- year: There has been a spike in percentage of serious accidents over the years. However, the percentage of not serious accidents has remained somewhat consistent
Other Visualizations¶
Due to the previous visualizations a comparison of certain variables was desired to see more correlations.
- Junction Control by Junction Detail
- Junction Control by Junction Location
- First point of Impact by Junction Detail
- First point of Impact by Junction Location
- Junction Control and First Point of Impact
Junction Control by Junction Detail¶
In [246]:
#Not Serious Accident
plt.figure(figsize=(20,15))
ax=sns.countplot("junction_control", hue="junction_detail",
palette="plasma", data=df)
plt.style.use('dark_background')
plt.title("Junction Control by Junction Detail",fontsize=25,fontweight="bold")
plt.xlabel("\nAccident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
# plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=False)
plt.savefig('junction_control_by_junction_detail.png')
plt.show()
Junction Control by Junction Location¶
In [245]:
plt.figure(figsize=(20,15))
ax=sns.countplot("junction_control", hue="junction_location",
palette="plasma", data=df)
plt.style.use('dark_background')
plt.title("Junction Control by Junction Location in Accidents",fontsize=25,fontweight="bold")
plt.xlabel("\nAccident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
# plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=False)
plt.savefig('junction_control_by_junction_location.png')
plt.show()
First point of Impact by Junction Detail¶
In [248]:
plt.figure(figsize=(20,15))
ax=sns.countplot("x1st_point_of_impact", hue="junction_detail",
palette="plasma", data=df)
plt.style.use('dark_background')
plt.title("First point of Impact by Junction Detail",fontsize=25,fontweight="bold")
plt.xlabel("\nAccident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
# plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=False)
plt.savefig('x1st_point_of_impact_by_junction_detail.png')
plt.show()
First point of Impact by Junction Location¶
In [247]:
plt.figure(figsize=(20,15))
ax=sns.countplot("x1st_point_of_impact", hue="junction_location",
palette="plasma", data=df)
plt.style.use('dark_background')
plt.title("First point of Impact by Junction Location",fontsize=25,fontweight="bold")
plt.xlabel("\nAccident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
# plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=False)
plt.savefig('x1st_point_of_impact_by_junction_location.png')
plt.show()
Junction Control and First Point of Impact¶
In [249]:
plt.figure(figsize=(20,15))
ax=sns.countplot("x1st_point_of_impact", hue="junction_control",
palette="plasma", data=df)
plt.style.use('dark_background')
plt.title("First point of Impact by Junction Control",fontsize=25,fontweight="bold")
plt.xlabel("\nAccident by Year", fontsize=15, fontweight="bold")
plt.legend().set_title('')
plt.legend(fontsize='22', loc = 'upper right')
plt.ylabel("\nNumber of Accidents", fontsize=15, fontweight="bold")
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.legend(fontsize='15', bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)
# plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(top=True, right=True, left=True, bottom=False)
plt.savefig('x1st_point_of_impact_by_junction_control.png')
plt.show()
Other Visualizations Summary¶
No matter the situation above, the most accidents were involving areas that were uncontrolled. One of the main ones were the junction Detail T or staggered junction.
Other areas of concern include Mid Junctions on roundabouts or main roads and areas approaching a junction were cars were either parking or waiting in the junction.
Solution¶
From the data above more controlled areas would be benefical. Maybe signs alerting drivers of the upcoming junctions, traffic lights, or stop signs would help in some of these areas where they are feasible.
For example, this is a staggered junction, the main junction detail in accidents. One can understand how a situation such as these can lead to numerous accidents especially if proper signage is not available. Perhaps traffic lights, stop signs, or warnings indicating that they are approaching certain junctions would help reduce accidents.
Web Scraping¶
Below you wll find a web scrape of the website, Learner Driving Centres, which contains information on road signs in the UK. They were pulled to show examples of signage available to be placed.
In [5]:
#request website
r = requests.get('https://www.learnerdriving.com/learn-to-drive/highway-code/road-signs')
#parse HTML
soup = BeautifulSoup(r.text, 'html.parser')
#filter results
results = soup.find_all('div', attrs={'class':'fifth'})
In [6]:
#done to find specific results area
first_result=results[0]
first_result
first_result.find('img')['src']
Out[6]:
In [7]:
#get images of signs and sign descriptions
signage = []
for result in results:
sign=result.find('img')['src']
sign_desc=result.contents[1]
signage.append((sign, sign_desc))
In [8]:
#put pulled UK Traffic Signs into dataframe
uktrafficsigns = pd.DataFrame(signage, columns=['Sign', 'Sign Description'])
uktrafficsigns.head()
Out[8]:
In [9]:
'''
the "image" is just part of the image link,
must parse the first half in order to have full image link
'''
uktrafficsigns['Sign'] = 'https://www.learnerdriving.com/'+uktrafficsigns['Sign']
uktrafficsigns.head()
Out[9]:
In [10]:
'''
In some coding below I saw that one of the fields was blank (at index 42) but was not reading as null.
In order to fix that I changed the "Sign Description" and decided to place it here.
'''
uktrafficsigns.at[42,'Sign Description']="T-junction with priority over vehicles from the right"
In [11]:
#I wanted to save this as a csv for later, and to stop unnecessary web scraping
uktrafficsigns.to_csv('uktrafficsigns.csv', header=False, index=False)
In [12]:
#I wanted the html to show up as images instead of links
def path_to_image_html(path):
return '<img src="'+ path + '" width="60" >'
pd.set_option('display.max_colwidth', -1)
ukts=HTML(uktrafficsigns.to_html(escape=False ,formatters=dict(Sign=path_to_image_html)))
HTML(uktrafficsigns.to_html(escape=False ,formatters=dict(Sign=path_to_image_html)))
Out[12]:
| Sign | Sign Description | |
|---|---|---|
| 0 |  |
Entry to 20 mph zone |
| 1 |  |
End of 20 mph zone |
| 2 |  |
Maximum speed |
| 3 |  |
National speed limit applies |
| 4 |  |
School crossing patrol |
| 5 |  |
Stop and give way |
| 6 |  |
Give way to traffic on major road |
| 7 |  |
Manually operated temporary |
| 8 |  |
STOP and GO signs |
| 9 |  |
No entry for vehicular traffic |
| 10 |  |
No vehicles except bicycles being pushed |
| 11 |  |
No cycling |
| 12 |  |
No motor vehicles |
| 13 |  |
No buses (over 8 passenger seats) |
| 14 |  |
No overtaking |
| 15 |  |
No towed caravans |
| 16 |  |
No vehicles carrying explosives |
| 17 |  |
No vehicle or combination of vehicles over length shown |
| 18 |  |
No vehicles over height shown |
| 19 |  |
No vehicles over width shown |
| 20 |  |
Give priority to vehicles from opposite direction |
| 21 |  |
No right turn |
| 22 |  |
No left turn |
| 23 |  |
No U-turns |
| 24 |  |
No goods vehicles over maximum gross weight shown (in tonnes) except for loading and unloading |
| 25 |  |
Ahead only |
| 26 |  |
Turn left ahead (right if symbol reversed) |
| 27 |  |
Turn left (right if symbol reversed) |
| 28 |  |
Keep left (right if symbol reversed) |
| 29 |  |
Vehicles may pass either side to reach same destination |
| 30 |  |
Mini-roundabout (roundabout circulation - give way to vehicles from the immediate right) |
| 31 |  |
Route to be used by pedal cycles only |
| 32 |  |
Segregated pedal cycle and pedestrian route |
| 33 |  |
Minimum speed |
| 34 |  |
End of minimum speed |
| 35 |  |
Distance to 'STOP' line ahead |
| 36 |  |
Dual carriage-way ends |
| 37 |  |
Road narrows on right (left if symbol reversed) |
| 38 |  |
Road narrows on both sides |
| 39 |  |
Distance to 'Give Way' line ahead |
| 40 |  |
Crossroads |
| 41 |  |
Junction on bend ahead |
| 42 |  |
T-junction with priority over vehicles from the right |
| 43 |  |
Staggered junction |
| 44 |  |
Traffic merging from left ahead |
| 45 |  |
Double bend first to left (symbol may be reversed) |
| 46 |  |
Bend to right (or left if symbol reversed) |
| 47 |  |
Roundabout |
| 48 |  |
Uneven road |
| 49 |  |
Plate below some signs |
| 50 |  |
Two-way traffic crosses one-way road |
| 51 |  |
Two-way traffic straight ahead |
| 52 |  |
Opening or swing bridge ahead |
| 53 |  |
Low-flying aircraft or sudden aircraft noise |
| 54 |  |
Falling or fallen rocks |
| 55 |  |
Traffic signals not in use |
| 56 |  |
Traffic signals |
| 57 |  |
Slippery road |
| 58 |  |
Steep hill downwards |
| 59 |  |
Steep hill upwards |
| 60 |  |
Tunnel ahead |
| 61 |  |
Trams crossing ahead |
| 62 |  |
Level crossing with barrier or gate ahead |
| 63 |  |
Level crossing without barrier or gate ahead |
| 64 |  |
Level crossing without barrier |
| 65 |  |
School crossing patrol ahead (some signs have amber lights which flash when children are crossing) |
| 66 |  |
Frail (or blind or disabled if shown) pedestrians likely to cross road ahead |
| 67 |  |
Pedestrians in road ahead |
| 68 |  |
Zebra crossing |
| 69 |  |
Overhead electric cable; plate indicates maximum height of vehicles which can pass safely |
| 70 |  |
Cattle |
| 71 |  |
Wild animals |
| 72 |  |
Wild horses or ponies |
| 73 |  |
Accompanied horses or ponies |
| 74 |  |
Cycle route ahead |
| 75 |  |
Risk of ice |
| 76 |  |
Traffic queues likely ahead |
| 77 |  |
Distance over which road humps extend |
| 78 |  |
Other danger; plate indicates nature of danger |
| 79 |  |
Soft verges |
| 80 |  |
Side winds |
| 81 |  |
Hump bridge |
| 82 |  |
Worded warning sign |
| 83 |  |
Quayside or river bank |
| 84 |  |
Risk of grounding |
In [18]:
'''
Here I am creating a df that will allow me to pull all junction signs.
"nction" was used instead of "junction" in order to pull all variables.
'''
junction =uktrafficsigns[uktrafficsigns['Sign Description'].str.contains("nction", regex=False)]
#Making it its own HTML object (same as above)
def path_to_image_html(path):
return '<img src="'+ path + '" width="60" >'
pd.set_option('display.max_colwidth', -1)
HTML(junction.to_html(escape=False ,formatters=dict(Sign=path_to_image_html)))
Out[18]:
| Sign | Sign Description | |
|---|---|---|
| 41 | |
Junction on bend ahead |
| 42 | |
T-junction with priority over vehicles from the right |
| 43 | |
Staggered junction |
In [14]:
#Repeated the above steps for giveways
give=uktrafficsigns[uktrafficsigns['Sign Description'].str.contains("ive ", regex=False)]
def path_to_image_html(path):
return '<img src="'+ path + '" width="60" >'
pd.set_option('display.max_colwidth', -1)
HTML(give.to_html(escape=False ,formatters=dict(Sign=path_to_image_html)))
Out[14]:
| Sign | Sign Description | |
|---|---|---|
| 5 | |
Stop and give way |
| 6 | |
Give way to traffic on major road |
| 20 | |
Give priority to vehicles from opposite direction |
| 30 | |
Mini-roundabout (roundabout circulation - give way to vehicles from the immediate right) |
| 39 | |
Distance to 'Give Way' line ahead |
In [15]:
#roundabouts
roundabout=uktrafficsigns[uktrafficsigns['Sign Description'].str.contains("ounda", regex=False)]
def path_to_image_html(path):
return '<img src="'+ path + '" width="60" >'
pd.set_option('display.max_colwidth', -1)
HTML(roundabout.to_html(escape=False ,formatters=dict(Sign=path_to_image_html)))
Out[15]:
| Sign | Sign Description | |
|---|---|---|
| 30 | |
Mini-roundabout (roundabout circulation - give way to vehicles from the immediate right) |
| 47 | |
Roundabout |
Mapping of Problem Area¶
Below is a mapping of Rural Areas, with serious accidents, without signage, in T or staggered junctions, in 2016. This was one of the more problematic areas according to the plotting above, so it is plotted below using latitude and longitude coordinates in order to look at areas that may need signage.
(The reason for such drilled down plotting is 1) detail 2) notebooks ability to handle such large data and plotting.)
In [2]:
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Genesis L. Taylor
Github | Linkedin | Tableau | genesisltaylor@gmail.com